ExoNet Database: Wearable Camera Images of Human Locomotion Environments

Abstract: Recent advances in computer vision and artificial intelligence have allowed researchers to develop environment recognition systems for robotic lower-limb exoskeletons and prostheses. However, small-scale and private training datasets have impeded the widespread development and dissemination of image classification algorithms for human locomotion environment recognition. To address these shortcomings, we developed “ExoNet” - the first open-source, large-scale hierarchical database of high-resolution wearable camera images of human locomotion environments. Unparalleled in both scale and diversity, ExoNet comprises over 5.6 million images of different indoor and outdoor real-world walking environments, which were collected using a lightweight wearable smartphone camera during the summer, fall, and winter seasons. Approximately 940,000 images in ExoNet were human-annotated using a 12-class hierarchical labelling architecture. Available publicly through IEEE DataPort, ExoNet offers an unprecedented community platform to train, develop, and compare next-generation image classification algorithms for human locomotion environment recognition. Beyond the control of lower-limb exoskeletons and prostheses, potential applications of ExoNet to humanoid and autonomous legged robotics.Reference: Laschowski B, McNally W, Wong A, and McPhee J. (2020). ExoNet Database: Wearable Camera Images of Human Locomotion Environments. Under Review.摘要：近年来，计算机视觉与人工智能领域的研究进展，使研究者得以开发面向机器人下肢外骨骼与假肢的环境识别系统。然而，小规模且私有化的训练数据集，却阻碍了人类行走环境识别图像分类算法的大规模开发与推广应用。 为解决上述不足，我们构建了ExoNet——全球首个开源、大规模层级化的人类行走环境可穿戴相机高分辨率图像数据库。该数据集在规模与多样性方面均无出其右，涵盖超560万张不同室内外真实行走场景的图像，采集工作采用轻量化可穿戴智能手机相机，覆盖夏季、秋季与冬季三个季节。其中约94万张图像采用12类层级标注架构完成人工标注。 ExoNet可通过IEEE DataPort平台公开获取，为面向人类行走环境识别的下一代图像分类算法的训练、开发与对比提供了前所未有的社区级研发平台。除用于下肢外骨骼与假肢的控制外，ExoNet还可在类人机器人与自主足式机器人领域发挥潜在应用价值。 参考文献：Laschowski B、McNally W、Wong A与McPhee J.（2020）.《ExoNet数据库：人类行走环境可穿戴相机图像》，待刊审稿中。